Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation
This addresses navigation challenges for UGVs in complex environments, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of robust traversability representation for unmanned ground vehicles (UGV) navigation in scenarios with irregular obstacles, introducing Circular Accessible Depth (CAD) and CADNet, which outperforms baselines in robustness and precision and performs well in real-world tests.
In this paper, we present the Circular Accessible Depth (CAD), a robust traversability representation for an unmanned ground vehicle (UGV) to learn traversability in various scenarios containing irregular obstacles. To predict CAD, we propose a neural network, namely CADNet, with an attention-based multi-frame point cloud fusion module, Stability-Attention Module (SAM), to encode the spatial features from point clouds captured by LiDAR. CAD is designed based on the polar coordinate system and focuses on predicting the border of traversable area. Since it encodes the spatial information of the surrounding environment, which enables a semi-supervised learning for the CADNet, and thus desirably avoids annotating a large amount of data. Extensive experiments demonstrate that CAD outperforms baselines in terms of robustness and precision. We also implement our method on a real UGV and show that it performs well in real-world scenarios.